Applying Delaunay Triangulation Augmentation for Deep Learning Facial Expression Generation and Recognition

Valev, Hristo; Gallucci, Alessio; Leufkens, Tim; Westerink, Joyce H. D. M.; Sas, Corina

doi:10.1007/978-3-030-68796-0_53

Cited by 2 publications

(1 citation statement)

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“…There are many reasons behind this, from the growth in computational power to the economic benefit of using a parametric model to simulate physical phenomena. Today, 3D models serve many fields, including animation of characters [ 1 ] and faces [ 2 , 3 , 4 ], recognition of expressions [ 5 ], face recognition [ 6 ], and inferring body shapes and measurement to be used, for example, in the clothing industry, for virtual try-on [ 7 ], or in the medical field to estimate fat distribution. However, the applications are often limited due to privacy and sensitive information constraints that reduce or block data sharing and aggregation from multiple sources.…”

Section: Introductionmentioning

confidence: 99%

Generating High-Resolution 3D Faces and Bodies Using VQ-VAE-2 with PixelSNAIL Networks on 2D Representations

Gallucci

Znamenskiy

Long

et al. 2023

Sensors

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Modeling and representing 3D shapes of the human body and face is a prominent field due to its applications in the healthcare, clothes, and movie industry. In our work, we tackled the problem of 3D face and body synthesis by reducing 3D meshes to 2D image representations. We show that the face can naturally be modeled on a 2D grid. At the same time, for more challenging 3D body geometries, we proposed a novel non-bijective 3D–2D conversion method representing the 3D body mesh as a plurality of rendered projections on the 2D grid. Then, we trained a state-of-the-art vector-quantized variational autoencoder (VQ-VAE-2) to learn a latent representation of 2D images and fit a PixelSNAIL autoregressive model to sample novel synthetic meshes. We evaluated our method versus a classical one based on principal component analysis (PCA) by sampling from the empirical cumulative distribution of the PCA scores. We used the empirical distributions of two commonly used metrics, specificity and diversity, to quantitatively demonstrate that the synthetic faces generated with our method are statistically closer to real faces when compared with the PCA ones. Our experiment on the 3D body geometry requires further research to match the test set statistics but shows promising results.

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